Sentiment Analysis with NLP using Python and Flask

Name: Sentiment Analysis with NLP using Python and Flask
Rating: 3.6 (258 reviews)

Along with a Project

Created byYaswanth Sai Palaghat

Last updated 1/2021

English

What you'll learn

Sentiment Analysis
Natural Language Processing
TextBlob
Building WebApps with Flask

Course content

6 sections • 16 lectures • 1h 25m total length

Introduction to the course1:49
Explore sentiment analysis with natural language processing to gauge customer satisfaction from reviews, using machine learning to classify reviews as positive, negative, or neutral, and analyze polarity and subjectivity.
About the Instructor0:27
Meet the instructor, a software developer and freelance writer who founded two small startups. Follow me on social media for updates on new courses and technologies.

Natural Language Processing3:58
Explore how natural language processing converts text and speech into machine readable data, enabling translation, voice recognition, and sentiment analysis in AI applications.
Sentiment Analysis2:57
Explore sentiment analysis with Python and Flask using natural language processing to identify emotions and polarity in text, and extract subjectivity insights from reviews and social media.
Need of Text Classification with example2:58
Explore how text classification converts unstructured data from reviews, emails, and social media into meaningful categories, enabling automated issue tagging and actionable business insights through sentiment analysis.
Process of Text Classification4:26
Explore the end-to-end process of text classification for sentiment analysis, from input data and text analytics to labeling and final output, using NLP and ML classifiers.

What we are building?3:38
Learn how to build a sentiment analysis app using NLP with Python and Flask, including tokenization, keyword extraction, and polarity and subjectivity scoring to channel reviews to the right teams.
Installations and Resources4:20
Create a sentiment analysis project in Python by organizing folders, installing NLP libraries, and configuring a Flask web app to run the sentiment analysis logic.

Initializing the Project4:16
initialize the project by setting up a Flask app in Python, import essential packages, configure templates, handle requests, and bootstrap the app to prepare for building a sentiment analysis interface.
Sentiment Analysis with TextBlob13:37
Learn to build a Flask-based sentiment analysis tool using TextBlob, handling routes and templates, and extracting polarity, subjectivity, token counts, and part-of-speech tags.

Flask UI-110:53
Design a Flask UI for sentiment analysis by rendering templates with Bootstrap, collecting user input, and submitting to an analyze method to display results.
Flask UI-218:05
Build a Flask UI for sentiment analysis by taking user input, analyzing tokens, polarity, and subjectivity, and displaying results in a Bootstrap-styled interface.
Flask UI-35:45
Write and wire up Flask UI-3 functions, including the index method and a button-driven analysis function. Manage a thousand-millisecond timing to clear the screen after execution.

Implementation4:43
Implement sentiment analysis by running pre-provided Python code in a Flask app, validating outputs locally via the terminal and browser at localhost, with troubleshooting tips.
Final Output2:27
Learn sentiment analysis with nlp using Python and Flask, including interpreting subjectivity and understandability scores, writing rules, debugging code, and applying project resources.
Youtube (Bonus)0:42
Explore the latest technologies, career development tips, interview preparation, digital marketing, and hands-on projects through a YouTube channel offering online courses and career guidance.

Requirements

Basics of Python programming

Description

Sentiment analysis is one of the most practical and exciting applications of Natural Language Processing (NLP) in today’s data-driven world. From analyzing customer reviews to monitoring social media opinions, businesses and researchers rely on sentiment analysis to understand emotions, trends, and feedback at scale. This course is designed to give you a hands-on introduction to building your own sentiment analysis system from scratch using Python.

We start by exploring the fundamentals of NLP and how machines can process and understand human language. You’ll then learn how to use TextBlob, a beginner-friendly NLP library, to classify text into positive, negative, or neutral sentiments. Through simple examples and guided coding sessions, you’ll see how sentiment analysis works in real-world scenarios such as product reviews and feedback.

Once you’ve mastered the basics, you’ll move on to building a fully functional Flask-based web application. This interactive app will allow users to input their own text reviews and instantly receive sentiment predictions. Along the way, you’ll gain experience in integrating machine learning models with web frameworks, a valuable skill for deploying AI-powered applications.

By the end of the course, you will have:

A strong understanding of sentiment analysis and its applications
Practical skills in using TextBlob for NLP tasks
Hands-on experience building and deploying a Flask web app

No prior experience in NLP is required — just basic Python knowledge and curiosity. Enroll today to combine AI and web development skills while creating your own real-world project!

Who this course is for:

Beginners who are curious about building projects with Machine Learning and NLP
Python Developers who want to build their first major project in NLP

Sentiment Analysis with NLP using Python and Flask

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 2min

Sentiment Analysis with NLP4 lectures • 14min

Starting the project2 lectures • 8min

Part-1 (TextBlob)2 lectures • 18min

Part-2 (Flask UI)3 lectures • 35min

Implementation and Output3 lectures • 8min

Requirements

Description

Who this course is for: